Selective Sampling of Phosphopeptides for Detection by MALDI Mass Spectrometry
Bibliographic record
Abstract
The analysis of phosphopeptides by mass spectrometry (MS) is one of the most challenging tasks in proteomics. This is due to the lower isoelectric point (pI) of phosphopeptides, which leads to inefficient sample ionization in MS, particularly when competing with other peptides. The problem is compounded by the typical low abundance of phosphopeptides in biological samples. We describe here a simple nonsorptive method to isolate phosphopeptides based on their pI. A voltage is applied to selectively migrate the phosphopeptides into a capillary, which are negatively charged at acidic pH. The selectively sampled fraction is directly deposited onto MALDI sample target in nanoliter volumes (7-35 nL) for highly sensitive MS detection. No significant sample loss is evident in this procedure; hence, the MS was able to detect the isolated phosphopeptides at trace quantity. In this case, attomole-level detection limit is achieved for synthetic phosphopeptides (nM concentration and nL volume), from a mixture containing other peptides at up to 1 million times higher in concentration. Selective sampling was also applied to the tryptic digest of beta- and alpha-caseins to reveal the multiple phosphorylated peptides at the low-femtomole level using MALDI MS. Knowledge of pI based on the rejection/injection of peptides was found to be useful in peak assignment. To confirm the sequence of the selectively sampled peptides, fraction collection was performed for offline ESI MS/MS analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".